def call_conv1d(self, other_args: List[str]):
"""Process conv1d command"""
try:
ns_parser = pred_helper.parse_args(
prog="conv1d",
description="""1D CNN.""",
other_args=other_args,
)
if ns_parser:
neural_networks_view.display_conv1d(
dataset=self.coin,
data=self.data[self.target],
n_input_days=ns_parser.n_inputs,
n_predict_days=ns_parser.n_days,
learning_rate=ns_parser.lr,
epochs=ns_parser.n_epochs,
batch_size=ns_parser.n_batch_size,
test_size=ns_parser.valid_split,
n_loops=ns_parser.n_loops,
no_shuffle=ns_parser.no_shuffle,
time_res=self.resolution,
)
except Exception as e:
console.print(e, "\n")
finally:
pred_helper.restore_env()
def call_lstm(self, other_args: List[str]):
"""Process lstm command"""
try:
ns_parser = pred_helper.parse_args(
prog="lstm",
description="""Long-Short Term Memory. """,
other_args=other_args,
)
if ns_parser:
neural_networks_view.display_lstm(
dataset=self.ticker,
data=self.df[self.target],
n_input_days=ns_parser.n_inputs,
n_predict_days=ns_parser.n_days,
learning_rate=ns_parser.lr,
epochs=ns_parser.n_epochs,
batch_size=ns_parser.n_batch_size,
test_size=ns_parser.valid_split,
n_loops=ns_parser.n_loops,
no_shuffle=ns_parser.no_shuffle,
)
except Exception as e:
logger.exception(str(e))
console.print(e, "\n")
finally:
pred_helper.restore_env()
def call_conv1d(self, other_args: List[str]):
"""Process conv1d command"""
try:
ns_parser = pred_helper.parse_args(
prog="conv1d",
description="""1D CNN.""",
other_args=other_args,
)
if ns_parser:
if ns_parser.n_inputs > len(self.data):
console.print(
f"[red]Data only contains {len(self.data)} samples and the model is trying "
f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with"
f" an earlier start date[/red]\n")
return
neural_networks_view.display_conv1d(
dataset=self.current_id,
data=self.data,
n_input_days=ns_parser.n_inputs,
n_predict_days=ns_parser.n_days,
learning_rate=ns_parser.lr,
epochs=ns_parser.n_epochs,
batch_size=ns_parser.n_batch_size,
test_size=ns_parser.valid_split,
n_loops=ns_parser.n_loops,
no_shuffle=ns_parser.no_shuffle,
time_res=self.resolution,
)
except Exception as e:
console.print(e, "\n")
finally:
pred_helper.restore_env()
def call_rnn(self, other_args: List[str]):
"""Process rnn command"""
try:
ns_parser = pred_helper.parse_args(
prog="rnn",
description="""Recurrent Neural Network. """,
other_args=other_args,
)
if ns_parser:
neural_networks_view.display_rnn(
dataset=self.ticker,
data=self.stock[self.target],
n_input_days=ns_parser.n_inputs,
n_predict_days=ns_parser.n_days,
learning_rate=ns_parser.lr,
epochs=ns_parser.n_epochs,
batch_size=ns_parser.n_batch_size,
test_size=ns_parser.valid_split,
n_loops=ns_parser.n_loops,
no_shuffle=ns_parser.no_shuffle,
)
except Exception as e:
console.print(e, "\n")
finally:
pred_helper.restore_env()
def call_mlp(self, other_args: List[str]):
"""Process mlp command"""
try:
ns_parser = pred_helper.parse_args(
prog="mlp",
description="""Multi-Layered-Perceptron. """,
other_args=other_args,
)
if not ns_parser:
return
neural_networks_view.display_mlp(
dataset=self.ticker,
data=self.stock[self.target],
n_input_days=ns_parser.n_inputs,
n_predict_days=ns_parser.n_days,
learning_rate=ns_parser.lr,
epochs=ns_parser.n_epochs,
batch_size=ns_parser.n_batch_size,
test_size=ns_parser.valid_split,
n_loops=ns_parser.n_loops,
no_shuffle=ns_parser.no_shuffle,
)
except Exception as e:
print(e, "\n")
finally:
pred_helper.restore_env()
def conv1d(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
"""
Train a 1D Convolutional Neural Net (1D CNN)
Parameters
----------
other_args:List[str]
Argparse arguments
s_ticker: str
Stock ticker
df_stock: pd.DataFrame
Dataframe of stock prices
"""
try:
ns_parser = parse_args(
prog="conv1d",
description="""1D CNN.""",
other_args=other_args,
)
if not ns_parser:
return
(
X_train,
X_valid,
y_train,
y_valid,
_,
_,
_,
y_dates_valid,
forecast_data_input,
dates_forecast_input,
scaler,
is_error,
) = prepare_scale_train_valid_test(df_stock["Adj Close"], ns_parser)
if is_error:
return
print(
f"Training on {X_train.shape[0]} sequences of length {X_train.shape[1]}. Using {X_valid.shape[0]} sequences "
f" of length {X_valid.shape[1]} for validation. Model will run {ns_parser.n_loops} loops"
)
future_dates = get_next_stock_market_days(dates_forecast_input[-1],
n_next_days=ns_parser.n_days)
preds = np.zeros(
(ns_parser.n_loops, X_valid.shape[0], ns_parser.n_days))
forecast_data = np.zeros((ns_parser.n_loops, ns_parser.n_days))
for i in range(ns_parser.n_loops):
# Build Neural Network model
model = build_neural_network_model(
cfg_nn_models.Convolutional,
ns_parser.n_inputs,
ns_parser.n_days,
)
model.compile(
optimizer=optimizers[cfg_nn_models.Optimizer](
learning_rate=ns_parser.lr),
loss=cfg_nn_models.Loss,
)
model.fit(
X_train.reshape(X_train.shape[0], X_train.shape[1], 1),
y_train,
epochs=ns_parser.n_epochs,
verbose=True,
batch_size=ns_parser.n_batch_size,
validation_data=(
X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1),
y_valid,
),
callbacks=[es],
)
preds[i] = model.predict(
X_valid.reshape(X_valid.shape[0], X_valid.shape[1],
1)).reshape(X_valid.shape[0], ns_parser.n_days)
forecast_data[i] = forecast(forecast_data_input, future_dates,
model, scaler).values.flat
forecast_data_df = pd.DataFrame(forecast_data.T, index=future_dates)
if ns_parser.n_loops > 1:
forecast_data_df["Median"] = forecast_data_df.median(axis=1)
print_pretty_prediction(forecast_data_df["Median"],
df_stock["Adj Close"].values[-1])
else:
print_pretty_prediction(forecast_data_df[0],
df_stock["Adj Close"].values[-1])
plot_data_predictions(
df_stock,
np.median(preds, axis=0),
y_valid,
y_dates_valid,
scaler,
f"Conv1D Model on {s_ticker}",
forecast_data_df,
ns_parser.n_loops,
)
print("")
except Exception as e:
print(e)
traceback.print_exc()
print("")
finally:
restore_env()
